Multiple Imputation with Chained equations in StatsModels library

This recipe describes what is Multiple Imputation with Chained Equations MICE in the StatsModels library

Recipe Objective - What is Multiple Imputation with Chained Equations (MICE) in the StatsModels library?

The MICE module can be used to fit most statistical models to datasets that are missing values for independent and/or dependent variables, providing strict standard error for tuned parameters. The basic idea is to treat the variable with the missing value as the dependent variable of the regression and some or all of the remaining variables as predictive variables. The MICE process iterates through these models, approximating each model in turn, and then uses a process called Predictive Average Matching (PMM) to generate a random drawing from the predicted distribution determined by the approximated model. To do. These random plots are the attribution values ​​of the attribution record. By default, each variable missing a variable is modeled using linear regression with the main effects of all other variables in the dataset. Note that the PMM procedure maintains the domain of each variable, even if the assignment model is linear. For example, if all observations for a particular variable are positive, then all assignments for that variable are always positive. The user also has the option to specify the model used to generate the assigned value for each variable.

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MICE:

Multiple Imputation with Chained Equations.

Parameters:

model_formula
The model formula to be fit to the imputed data sets. This formula is for the ‘analysis model.’

model_class
The model to be fit to the imputed data sets. This model class is for the ‘analysis model.’

data
MICEData object containing the data set for which missing values will be imputed

Example:

# Importing libraries
import statsmodels.api as sm
import statsmodels.imputation.mice as mice
from statsmodels.imputation.mice import MICEData
from statsmodels.imputation.mice import MICE

# Importing moore dataset from carData package
X = sm.datasets.get_rdataset("flchain", "survival").data

# Converting into MICEData
X = MICEData(X[['creatinine','age','futime']])

# Fitting MICE model
mice = MICE("creatinine ~ age + futime", sm.OLS, X)
model = mice.fit()

# Model summary
model.summary()

Output-
Method:	MICE	Sample size:	7874
Model:	OLS	Scale	0.16
Dependent variable:	creatinine	Num. imputations	10
Coef.	Std.Err.	t	P>|t|	[0.025	0.975]	FMI
Intercept	1.1318	0.0442	25.5785	0.0000	1.0450	1.2185	0.3274
age	0.0020	0.0005	3.8530	0.0001	0.0010	0.0031	0.2427
futime	-0.0000	0.0000	-11.8670	0.0000	-0.0001	-0.0000	0.2832

In this way, we can perform MICE in the StatsModels library.

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